Spatial analysis to quantify numerical model bias and
dependence: How many climate models are there?

Mikyoung Jun
Texas A&M University

Abstract
A limited number of complex numerical models that simulate the
Earth's atmosphere, ocean and land processes are the primary tool to study
how climate may change over the next century due to
anthropogenic emissions of greenhouse gases. One standard
assumption is that these climate models are random samples from a
distribution of possible models centered around the true climate. This
implies that agreement
with observations and the predictive skill of climate models will improve
as more models are added to an average of the models. In this paper, we
present a statistical methodology to
quantify whether climate models are indeed unbiased and
whether and where model biases are correlated across models.
We consider the simulated mean state and the simulated trend over the
period 1970-1999 for Northern Hemisphere summer and winter temperature.
The key to the statistical analysis is
a spatial model for the bias of each climate model and the use of kernel
smoothing to estimate the correlations of biases across
different climate models. The spatial model is particularly important to
determine statistical significance of the estimated correlations under the
hypothesis of independent climate models.
Our results suggest that most of the climate
model bias patterns are indeed correlated. In particular, climate models
developed by the same institution have highly correlated biases.
Also somewhat surprisingly we find evidence that the model skills for
simulating the mean climate and simulating the warming trends are not
strongly related.